The aim of this research was to compare different Bayesian procedures to integrate information from outside a given evaluation system, hereafter called external information, and in this context estimated breeding values (EBV), into this genetic evaluation, hereafter called internal evaluation, and to improve the Bayesian procedures to assess their potential to combine information from diverse sources. The 2 improvements were based on approximations of prior mean and variance. The first version of modified Bayesian evaluation considers all animals as animals associated with external information. For animals that have no external information (i.e., internal animals), external information is predicted from available external information. Thereby, propagation of this external information through the whole pedigree is allowed. Furthermore, the prediction of external information for internal animals allows large simplifications of the computational burden during setup and solving of mixed model equations. However, double counting among external animals (i.e., animals associated with available external information) is not avoided. Double counting concerns multiple considerations of contributions due to relationships by integration of external EBV for related external animals and is taken into account by the second version of modified Bayesian evaluation. This version includes the estimation of double counting before integration of external information. To test the improvements, 2 dairy cattle populations were simulated across 5 generations. Milk production for the first lactation for each female was simulated in both populations. Internal females were randomly mated with internal males and 50 external males. Results for 100 replicates showed that rank correlations among Bayesian EBV and EBV based on the joint use of external and internal data were very close to 1 for both external and internal animals if all internal and external animals were associated with external information. The respective correlations for the internal evaluation were equal to 0.54 and 0.95 if no external information was integrated. If double counting was avoided, mean squared error, expressed as a percentage of the internal mean squared error, was close to zero for both external and internal animals. However, computational demands increased when double counting was avoided. Finally, the improved Bayesian procedures have the potential to be applied for integrating external EBV, or even genomic breeding values following some additional